An Assessment of a Model for Error Processing in the CMS Data Acquisition System

The CMS Data Acquisition System consists of O(20000) interdependent services. A system providing exception and application-specific monitoring data is essential for the operation of such a cluster. Due to the number of involved services the amount of monitoring data is higher than a human operator can handle efficiently. Thus moving the expert-knowledge for error analysis from the operator to a dedicated system is a natural choice. This reduces the number of notifications to the operator for simpler visualization and provides meaningful error cause descriptions and suggestions for possible countermeasures. This paper discusses an architecture of a workflow-based hierarchical error analysis system based on Guardians for the CMS Data Acquisition System. Guardians provide a common interface for error analysis of a specific service or subsystem. To provide effective and complete error analysis, the requirements regarding information sources, monitoring and configuration, are analyzed. Formats for common notification types are defined and a generic Guardian based on Event-Condition-Action rules is presented as a proof-of-concept

Explainable machine learning in soil mapping: Peeking into the black box

Während des Anthropozäns und insbesondere in den letzten Jahrzehnten hat sich die Umwelt der Erde stark verändert. Die planetarischen Grenzen stehen zunehmend unter Druck. Da der Boden als wichtiger Teil der Kohlenstoff- und Stickstoffkreisläufe das Klima beeinflusst, ist er eine wichtige Ressource bei der Bewältigung dieser Umweltprobleme. Folglich spielt das Wissen über den Boden, Bodenprozesse und Bodenfunktionen eine wesentliche Rolle bei der Erforschung und Lösung dieser schwerwiegenden ökologischen und sozioökonomischen Herausforderungen. Die Kartierung und Modellierung des Bodens liefert räumliche Kenntnis über den Zustand des Bodens und seine Veränderungen im Laufe der Zeit. Dies ermöglicht es, Methoden der Bodenbewirtschaftung und Lösungsansätze für Umweltprobleme zu beurteilen und zu bewerten. Methoden des maschinellen Lernens haben sich für die räumliche Kartierung und Modellierung des Bodens als geeignet erwiesen. Oft handelt es sich dabei aber um Black Boxes und die Modellentscheidungen und -ergebnisse werden nicht erklärt. Allerdings würden erklärbare Bodenmodelle auf der Grundlage des maschinellen Lernens die Erkennung von Umweltveränderungen erleichtern, zur Entscheidungsfindung für den Umweltschutz beitragen und die Akzeptanz von Wissenschaft, Politik in Gesellschaft fördern. Daher sind die jüngsten Bemühungen im Bereich des maschinellen Lernens darauf ausgerichtet, den konventionellen Rahmen des maschinellen Lernens auf er¬klärbares maschinelles Lernen zu erweitern, um 1) Entscheidungen zu begründen, 2) die Modelle besser zu steuern und 3) zu verbessern und 4) neues Wissen zu generieren. Die Kernelemente für erklärbares maschinelles Lernen sind Transparenz, Interpretierbarkeit und Erklärbarkeit. Darüber hinaus sind domain knowledge und wissenschaftliche Konsistenz entscheidend. Bei der Bodenmodellierung spielten die Konzepte des erklärbaren maschinellen Lernens jedoch bisher eine geringe Rolle. Ziel dieser Arbeit war es, zu untersuchen und zu beschreiben, wie Transparenz, Interpretierbarkeit und Erklärbarkeit im Rahmen der Bodenmodellierung erreicht werden können. Die Fallbeispiele zeigten, wie Konsistenz mit Modellvergleichen bewertet werden kann und domain knowledge in die Modelle einfließt. Ebenso zeigten die Studien, wie Transparenz mit reproduzierbarer Proben- und Variablenauswahl erreicht werden kann und wie die Interpretation der Modelle mit domain knowledge verknüpft werden kann, um die Modellergebnisse besser zu erklären und in Bezug zu bodenkundlichem Wissen zu setzen sind.During the Anthropocene and especially in the past decades earth’s environment has undergone major changes. The planetary boundaries are increasingly under pressure. Since soil affects climate as compartment of the carbon and nitrogen cycles, it is an important resource in approaching these environmental problems. Consequently, knowledge about soil, soil processes and soil functions plays an essential role in research on and solutions for these severe environmental and socio-economic challenges. The mapping and modelling of soil provides spatial knowledge of soil status and changes over time, which allows to assess and evaluate soil management practices and attempts to solve to environmental problems. Machine learning methods have proven to be suitable for spatial mapping and modelling of soil, but often are black boxes and the model decisions and prediction results remain unexplained. However, explainable soil models based on machine learning would facilitate detection of environmental changes, contribute to decision making for environmental protection and foster acceptance in science, politics, and society. Therefore, latest efforts in machine learning were to expand the conventional machine learning framework to explainable machine learning to 1) justify decisions, 2) control, and 3) improve models and 4) to discover new knowledge. The core elements for explainable machine learning are transparency, interpretability and explainability. Additionally, domain knowledge and scientific consistency are crucial. However, to date the concepts of explainable machine learning played a marginal role in soil modelling and mapping. Objective of this thesis was to explore and describe how transparency, interpretability and explainability can be achieved in the soil mapping framework. The example studies showed how scientific consistency can be evaluated with model comparison and domain knowledge was and incorporated in DSM models. The studies showed how transparency can be accomplished with reproducible sample and covariate selection, and how interpretation of the models can be linked with domain knowledge about soil formation and processes to explain the model results

Proceedings of the 2021 DigitalFUTURES

This open access book is a compilation of selected papers from 2021 DigitalFUTURES—The 3rd International Conference on Computational Design and Robotic Fabrication (CDRF 2021). The work focuses on novel techniques for computational design and robotic fabrication. The contents make valuable contributions to academic researchers, designers, and engineers in the industry. As well, readers encounter new ideas about understanding material intelligence in architecture

Addressing the evolution of automated user behaviour patterns by runtime model interpretation

The final publication is available at Springer via http://dx.doi.org/10.1007/s10270-013-0371-3The use of high-level abstraction models can facilitate and improve not only system development but also runtime system evolution. This is the idea of this work, in which behavioural models created at design time are also used at runtime to evolve system behaviour. These behavioural models describe the routine tasks that users want to be automated by the system. However, users¿ needs may change after system deployment, and the routine tasks automated by the system must evolve to adapt to these changes. To facilitate this evolution, the automation of the specified routine tasks is achieved by directly interpreting the models at runtime. This turns models into the primary means to understand and interact with the system behaviour associated with the routine tasks as well as to execute and modify it. Thus, we provide tools to allow the adaptation of this behaviour by modifying the models at runtime. This means that the system behaviour evolution is performed by using high-level abstractions and avoiding the costs and risks associated with shutting down and restarting the system.This work has been developed with the support of MICINN, under the project EVERYWARE TIN2010-18011, and the support of the Christian Doppler Forschungsgesellschaft and the BMWFJ, Austria.Serral Asensio, E.; Valderas Aranda, PJ.; Pelechano Ferragud, V. (2013). Addressing the evolution of automated user behaviour patterns by runtime model interpretation. Software and Systems Modeling. https://doi.org/10.1007/s10270-013-0371-3SWeiser, M.: The computer of the 21st century. Sci. Am. 265, 66–75 (1991)Serral, E., Valderas, P., Pelechano, V.: Context-adaptive coordination of pervasive services by interpreting models during runtime. Comput. 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Hyperspectral Imaging for Fine to Medium Scale Applications in Environmental Sciences

The aim of the Special Issue “Hyperspectral Imaging for Fine to Medium Scale Applications in Environmental Sciences” was to present a selection of innovative studies using hyperspectral imaging (HSI) in different thematic fields. This intention reflects the technical developments in the last three decades, which have brought the capacity of HSI to provide spectrally, spatially and temporally detailed data, favoured by e.g., hyperspectral snapshot technologies, miniaturized hyperspectral sensors and hyperspectral microscopy imaging. The present book comprises a suite of papers in various fields of environmental sciences—geology/mineral exploration, digital soil mapping, mapping and characterization of vegetation, and sensing of water bodies (including under-ice and underwater applications). In addition, there are two rather methodically/technically-oriented contributions dealing with the optimized processing of UAV data and on the design and test of a multi-channel optical receiver for ground-based applications. All in all, this compilation documents that HSI is a multi-faceted research topic and will remain so in the future

Self-managed Workflows for Cyber-physical Systems

Workflows are a well-established concept for describing business logics and processes in web-based applications and enterprise application integration scenarios on an abstract implementation-agnostic level. Applying Business Process Management (BPM) technologies to increase autonomy and automate sequences of activities in Cyber-physical Systems (CPS) promises various advantages including a higher flexibility and simplified programming, a more efficient resource usage, and an easier integration and orchestration of CPS devices. However, traditional BPM notations and engines have not been designed to be used in the context of CPS, which raises new research questions occurring with the close coupling of the virtual and physical worlds. Among these challenges are the interaction with complex compounds of heterogeneous sensors, actuators, things and humans; the detection and handling of errors in the physical world; and the synchronization of the cyber-physical process execution models. Novel factors related to the interaction with the physical world including real world obstacles, inconsistencies and inaccuracies may jeopardize the successful execution of workflows in CPS and may lead to unanticipated situations. This thesis investigates properties and requirements of CPS relevant for the introduction of BPM technologies into cyber-physical domains. We discuss existing BPM systems and related work regarding the integration of sensors and actuators into workflows, the development of a Workflow Management System (WfMS) for CPS, and the synchronization of the virtual and physical process execution as part of self-* capabilities for WfMSes. Based on the identified research gap, we present concepts and prototypes regarding the development of a CPS WFMS w.r.t. all phases of the BPM lifecycle. First, we introduce a CPS workflow notation that supports the modelling of the interaction of complex sensors, actuators, humans, dynamic services and WfMSes on the business process level. In addition, the effects of the workflow execution can be specified in the form of goals defining success and error criteria for the execution of individual process steps. Along with that, we introduce the notion of Cyber-physical Consistency. Following, we present a system architecture for a corresponding WfMS (PROtEUS) to execute the modelled processes-also in distributed execution settings and with a focus on interactive process management. Subsequently, the integration of a cyber-physical feedback loop to increase resilience of the process execution at runtime is discussed. Within this MAPE-K loop, sensor and context data are related to the effects of the process execution, deviations from expected behaviour are detected, and compensations are planned and executed. The execution of this feedback loop can be scaled depending on the required level of precision and consistency. Our implementation of the MAPE-K loop proves to be a general framework for adding self-* capabilities to WfMSes. The evaluation of our concepts within a smart home case study shows expected behaviour, reasonable execution times, reduced error rates and high coverage of the identified requirements, which makes our CPS~WfMS a suitable system for introducing workflows on top of systems, devices, things and applications of CPS.:1. Introduction 15 1.1. Motivation 15 1.2. Research Issues 17 1.3. Scope & Contributions 19 1.4. Structure of the Thesis 20 2. Workflows and Cyber-physical Systems 21 2.1. Introduction 21 2.2. Two Motivating Examples 21 2.3. Business Process Management and Workflow Technologies 23 2.4. Cyber-physical Systems 31 2.5. Workflows in CPS 38 2.6. Requirements 42 3. Related Work 45 3.1. Introduction 45 3.2. Existing BPM Systems in Industry and Academia 45 3.3. Modelling of CPS Workflows 49 3.4. CPS Workflow Systems 53 3.5. Cyber-physical Synchronization 58 3.6. Self-* for BPM Systems 63 3.7. Retrofitting Frameworks for WfMSes 69 3.8. Conclusion & Deficits 71 4. Modelling of Cyber-physical Workflows with Consistency Style Sheets 75 4.1. Introduction 75 4.2. Workflow Metamodel 76 4.3. Knowledge Base 87 4.4. Dynamic Services 92 4.5. CPS-related Workflow Effects 94 4.6. Cyber-physical Consistency 100 4.7. Consistency Style Sheets 105 4.8. Tools for Modelling of CPS Workflows 106 4.9. Compatibility with Existing Business Process Notations 111 5. Architecture of a WfMS for Distributed CPS Workflows 115 5.1. Introduction 115 5.2. PROtEUS Process Execution System 116 5.3. Internet of Things Middleware 124 5.4. Dynamic Service Selection via Semantic Access Layer 125 5.5. Process Distribution 126 5.6. Ubiquitous Human Interaction 130 5.7. Towards a CPS WfMS Reference Architecture for Other Domains 137 6. Scalable Execution of Self-managed CPS Workflows 141 6.1. Introduction 141 6.2. MAPE-K Control Loops for Autonomous Workflows 141 6.3. Feedback Loop for Cyber-physical Consistency 148 6.4. Feedback Loop for Distributed Workflows 152 6.5. Consistency Levels, Scalability and Scalable Consistency 157 6.6. Self-managed Workflows 158 6.7. Adaptations and Meta-adaptations 159 6.8. Multiple Feedback Loops and Process Instances 160 6.9. Transactions and ACID for CPS Workflows 161 6.10. Runtime View on Cyber-physical Synchronization for Workflows 162 6.11. Applicability of Workflow Feedback Loops to other CPS Domains 164 6.12. A Retrofitting Framework for Self-managed CPS WfMSes 165 7. Evaluation 171 7.1. Introduction 171 7.2. Hardware and Software 171 7.3. PROtEUS Base System 174 7.4. PROtEUS with Feedback Service 182 7.5. Feedback Service with Legacy WfMSes 213 7.6. Qualitative Discussion of Requirements and Additional CPS Aspects 217 7.7. Comparison with Related Work 232 7.8. Conclusion 234 8. Summary and Future Work 237 8.1. Summary and Conclusion 237 8.2. Advances of this Thesis 240 8.3. Contributions to the Research Area 242 8.4. Relevance 243 8.5. Open Questions 245 8.6. Future Work 247 Bibliography 249 Acronyms 277 List of Figures 281 List of Tables 285 List of Listings 287 Appendices 28